SoroushVahidi/scidocs huggingface dataset:
Immersive VR for Scientific Visualization: A Progress Report
(2000)

potential to be a powerful tool for the visualization of burgeoning scientific data sets and models. In this article we sketch a research agenda for the hardware and software technology underlying IVR for scientific visualization. In contrast to Brooks’ excellent survey last year, which reported on the state of IVR and provided concrete examples of its production use, this article is somewhat speculative. We don’t present solutions but rather a progress report, a hope, and a call to action, to help scientists cope with a major crisis that threatens to impede their progress. Brooks’ examples show that the technology has only recently started to mature—in his words, it “barely works.” IVR is used for walkthroughs of buildings and other structures, virtual prototyping (vehicles such as cars, tractors, and airplanes), medical applications (surgical visualization, planning, and training), “experiences” applied as clinical therapy (reliving Vietnam experiences to treat post-traumatic stress disorder, treating agoraphobia), and entertainment. Building on Brooks’ work, here we concentrate on why scientific visualization is also a good application area for IVR. First we’ll briefly review scientific visualization as a means of understanding models and data, then discuss the problem of exploding data set size, both from sensors and from simulation runs, and the consequent demand for new approaches. We see IVR as part of the solution: as a richer visualization and interaction environment, it can potentially enhance the scientist’s ability to manipulate the levels of abstraction necessary for multi-terabyte and petabyte data sets and to formulate hypotheses to guide very long simulation runs. In short, IVR has the potential to facilitate a more balanced human-computer partnership that maximizes bandwidth to the brain by more fully engaging the human sensorium. We argue that IVR remains in a primitive state of development and is, in the case of CAVEs and tiled projection displays, very expensive and therefore not in routine use. (We use the term cave to denote both the original CAVE developed at the University of Illinois’ Electronic Visualization Laboratory and CAVE-style derivatives.) Evolving hardware and software technology may, however, enable IVR to become as ubiquitous as 3D graphics workstations—once exotic and very expensive—are today. Finally, we describe a research agenda, first for the technologies that enable IVR and then for the use of IVR for scientific visualization. Punctuating the discussion are sidebars giving examples of scientific IVR work currently under way at Brown University that addresses some of the research challenges, as well as other sidebars on data set size growth and IVR interaction metaphors.
Establishment of tumor-specific copy number alterations from plasma DNA of patients with cancer
(2013)

With the increasing number of available predictive biomarkers, clinical management of cancer is becoming increasingly reliant on the accurate serial monitoring of tumor genotypes. We tested whether tumor-specific copy number changes can be inferred from the peripheral blood of patients with cancer. To this end, we determined the plasma DNA size distribution and the fraction of mutated plasma DNA fragments with deep sequencing and an ultrasensitive mutation-detection method, i.e., the Beads, Emulsion, Amplification, and Magnetics (BEAMing) assay. When analyzing the plasma DNA of 32 patients with Stage IV colorectal carcinoma, we found that a subset of the patients (34.4%) had a biphasic size distribution of plasma DNA fragments that was associated with increased circulating tumor cell numbers and elevated concentration of mutated plasma DNA fragments. In these cases, we were able to establish genome-wide tumor-specific copy number alterations directly from plasma DNA. Thus, we could analyze the current copy number status of the tumor genome, which was in some cases many years after diagnosis of the primary tumor. An unexpected finding was that not all patients with progressive metastatic disease appear to release tumor DNA into the circulation in measurable quantities. When we analyzed plasma DNA from 35 patients with metastatic breast cancer, we made similar observations suggesting that our approach may be applicable to a variety of tumor entities. This is the first description of such a biphasic distribution in a surprisingly high proportion of cancer patients which may have important implications for tumor diagnosis and monitoring.
SEISA: set expansion by iterative similarity aggregation
(2011)

In this paper, we study the problem of expanding a set of given seed entities into a more complete set by discovering other entities that also belong to the same concept set. A typical example is to use "Canon" and "Nikon" as seed entities, and derive other entities (e.g., "Olympus") in the same concept set of camera brands. In order to discover such relevant entities, we exploit several web data sources, including lists extracted from web pages and user queries from a web search engine. While these web data are highly diverse with rich information that usually cover a wide range of the domains of interest, they tend to be very noisy. We observe that previously proposed random walk based approaches do not perform very well on these noisy data sources. Accordingly, we propose a new general framework based on iterative similarity aggregation, and present detailed experimental results to show that, when using general-purpose web data for set expansion, our approach outperforms previous techniques in terms of both precision and recall.
A simple intrusion detection method for controller area network
(2016)

The Controller Area Network (CAN) is established as the main communication channel inside vehicles. CAN relies on frame broadcast to share data between different microcontrollers managing critical or comfort functions such as cruise control or air conditioning. CAN is distinguished by its simplicity, its real-time application compatibility and its low deployment cost. However, CAN major drawback is its lack of security support. That is, CAN fails to provide protections against attacks such as intrusion, denial of service or impersonation. We propose in this work a simple intrusion detection method for CAN. Our main idea is to make each microcontroller monitor the CAN bus to detect malicious frames.
One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction
(2015)

Supervised word sense disambiguation (WSD) systems are usually the best performing systems when evaluated on standard benchmarks. However, these systems need annotated training data to function properly. While there are some publicly available open source WSD systems, very few large annotated datasets are available to the research community. The two main goals of this paper are to extract and annotate a large number of samples and release them for public use, and also to evaluate this dataset against some word sense disambiguation and induction tasks. We show that the open source IMS WSD system trained on our dataset achieves stateof-the-art results in standard disambiguation tasks and a recent word sense induction task, outperforming several task submissions and strong baselines.
Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals
(2009)

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals. That men are machines (whatever else they may be) has long been suspected; but not till our generation have men fairly felt in concrete just what wonderful psycho-neuro-physical mechanisms they are. William James (1893; 1842 – 1910) A. Fred, J. Filipe, and H. Gamboa (Eds.): BIOSTEC 2009, CCIS 52, pp. 21–47, 2010. c © Springer-Verlag Berlin Heidelberg 2010 22 E.L. van den Broek et al.
Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information
(2016)

In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts, but no research has put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intraand inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 14.3 percent over the model using only the SKU’s own predictors. But of the improvements achieved, 88.1 percent of it comes from the intra-category information, and only 11.9 percent from the inter-category information. The substantive marketing results also have implications for promotional category management.
Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs
(2016)

In this paper, we present extensive advances in live-fly field experimentation capabilities of large numbers of fixed-wing aerial robots, and highlight both the enabling technologies as well as the challenges addressed in such largescale flight operations. We showcase results from recent field tests, including the autonomous launch, flight, and landing of 50 UAVs, which illuminate numerous operational lessons learned and generate rich multi-UAV datasets. We detail the design and open architecture of the testbed, which intentionally leverages low-cost and open-source components, aimed at promoting continued advances and alignment of multi-robot systems research and practice.
Model-based dynamic gait generation for a leg-wheel transformable robot
(2015)

We report on the model-based approach to dynamic trotting and pronking gait generation for a leg-wheel transformable robot. The rolling spring-loaded inverted pendulum (R-SLIP) model served as the template for robot locomotion by programming the robot's motion according to the stable fixed-point trajectories of the model. Two strategies are developed to match the robot leg motions to the virtual model leg: First, using the two active degrees of freedom on each leg to simulate the passive spring effect of the R-SLIP model leg. Second, installing a torsion spring on the leg-wheel module to render the leg-wheel morphology identical to that of the model leg. This model-based approach to dynamic behavior generation for the robot is experimentally evaluated. The robot can successfully generate an R-SLIP-like stable pronking gait with a flight phase.
MobiCloud: A geo-distributed mobile cloud computing platform
(2012)

In a cloud computing environment, users prefer to migrate their locally processing workloads onto the cloud where more resources with better performance can be expected. ProtoGENI [1] and PlanetLab [17] have further improved the current Internet-based resource outsourcing by allowing end users to construct a virtual network system through virtualization and programmable networking technologies. However, to the best of our knowledge, there is no such general service or resource provisioning platform designated for mobile devices. In this paper, we present a new design and implementation of MobiCloud that is a geo-distributed mobile cloud computing platform. The discussions of the system components, infrastructure, management, implementation flow, and service scenarios are followed by an example on how to experience the MobiCloud system.
Toward Enabling Automated Cognition and Decision-Making in Complex Cyber-Physical Systems
(2018)

This article presents a framework for empowering automated cognition and decision making in complex cyber-physical systems (CPS). The key idea is for each cyber-physical component in the system to be able to construct from observations and actions a reduced model of its behavior and respond to external stimuli in the form of a (timed) probabilistic automaton such as a semi-Markov decision process. The reduced order behavioral models of various cyber-physical components can then be integrated into an optimization and decision-making module to determine the best actions for the components under different operating scenarios and cost / payoff functions by solving a bounded-rationality global decision-making problem.1
Gossip-based Causal Order Delivery Protocol Respecting Deadline Constraints in Publish/Subscribe Systems
(2013)

Publish/subscribe systems based on gossip protocols are elastically to scale in and out and provides suitable consistency guarantees for data safety and high availability but, does not deal with end-to-end message delay and message order-based consistency. Especially in real-time collaborative applications, it is possible for the messages to take each a different time to arrive at end users. So, these applications should be based on P/S infrastructure including dealing with message deadlines and message ordering consistencies. Gossip communication is becoming one of the promising solutions for addressing P/S scalability problems in providing information propagation functionality by exploiting a mixture of diverse consistency options. In this paper, we present a new causal order protocol based on scalable P/S architecture for real-time collaborative applications in social web platforms to guarantee causally ordered message delivery, respecting deadline-constraints from brokers to subscribers. In the proposed protocol, every broker manages a 2-dimensional vector, representing its knowledge of the last message sent by each broker at a certain time. But, every broker disseminates a multicast message with a 1-dimensional vector, the time-stamped information that represents the maximum number of gossip rounds to subscribers because all messages disseminated by brokers have the same deadline as the maximum number of gossip rounds. Therefore, the proposed protocol for P/S based on gossiping results in very low communication overhead from brokers to subscribers in the context of respecting deadline-constraints.
Digital image integrity - a survey of protection and verification techniques
(2017)

We are currently on a verge of a revolution in digital photography. Developments in computational imaging and adoption of artificial intelligence have spawned new editing techniques that give impressive results in astonishingly short timeframes. The advent of multi-sensor and multi-lens cameras will further challenge many existing integrity verification techniques. As a result, it will be necessary to re-evaluate our notion of image authenticity and look for new techniques that could work efficiently in this new reality. The goal of this paper is to thoroughly review existing techniques for protection and verification of digital image integrity. In contrast to other recent surveys, the discussion covers the most important developments both in active protection and in passive forensic analysis techniques. Existing approaches are analyzed with respect to their capabilities, fundamental limitations, and prospective attack vectors. Whenever possible, the discussion is supplemented with real operation examples and a list of available implementations. Finally, the paper reviews resources available in the research community, including public data-sets and commercial or open-source software. The paper concludes by discussing relevant developments in computational imaging and highlighting future challenges and open research problems.
Robust multivariate autoregression for anomaly detection in dynamic product ratings
(2014)

User provided rating data about products and services is one key feature of websites such as Amazon, TripAdvisor, or Yelp. Since these ratings are rather static but might change over time, a temporal analysis of rating distributions provides deeper insights into the evolution of a products' quality. Given a time-series of rating distributions, in this work, we answer the following questions: (1) How to detect the base behavior of users regarding a product's evaluation over time? (2) How to detect points in time where the rating distribution differs from this base behavior, e.g., due to attacks or spontaneous changes in the product's quality? To achieve these goals, we model the base behavior of users regarding a product as a latent multivariate autoregressive process. This latent behavior is mixed with a sparse anomaly signal finally leading to the observed data. We propose an efficient algorithm solving our objective and we present interesting findings on various real world datasets.
Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications
(2010)

This paper summarizes results on a recently introduced family of Datalog-based languages, called Datalog+/-, which is a new framework for tractable ontology querying, and for a variety of other applications. Datalog+/- extends plain Datalog by features such as existentially quantified rule heads and, at the same time, restricts the rule syntax so as to achieve decidability and tractability. In particular, we discuss three paradigms ensuring decidability: chase termination, guardedness, and stickiness.
Estimating 3D Egomotion from Perspective Image Sequence
(1990)

This paper deals with the computation of sensor motion from sets of displacement vectors obtained from consecutive pairs of images. The problem is investigated with emphasis on its application to autonomous robots and land vehicles. First, the effects of 3-D camera rotation and translation upon the observed image are discussed and in particular the concept of the focus of expansion (FOE). It is shown that locating the FOE precisely is difficult when displacement vectors are corrupted by noise and errors. A more robust performance can be achieved by computing a 2-D region of possible FOE-locations (termed the fuzzy FOE) instead of looking for a single-point FOE. The shape of this FOE-region is an explicit indicator for the accuracy of the result. It has been shown elsewhere that given the fuzzy FOE, a number of powerful inferences about the 3-D scene structure and motion become possible. This paper concentrates on the aspects of computing the fuzzy FOE and shows the performance of a particular algorithm on real motion sequences taken from a moving autonomous land vehicle. Zndex Tenns-Autonomous mobile robot, dynamic scene analysis, fuzzy focus of expansion, motion analysis, passive navigation, sensor motion estimation.
Container Port Performance Measurement and Comparison Leveraging Ship GPS Traces and Maritime Open Data
(2016)

Container ports are generally measured and compared using performance indicators such as container throughput and facility productivity. Being able to measure the performance of container ports quantitatively is of great importance for researchers to design models for port operation and container logistics. Instead of relying on the manually collected statistical information from different port authorities and shipping companies, we propose to leverage the pervasive ship GPS traces and maritime open data to derive port performance indicators, including ship traffic, container throughput, berth utilization, and terminal productivity. These performance indicators are found to be directly related to the number of container ships arriving at the terminals and the number of containers handled at each ship. Therefore, we propose a framework that takes the ships' container-handling events at terminals as the basis for port performance measurement. With the inferred port performance indicators, we further compare the strengths and weaknesses of different container ports at the terminal level, port level, and region level, which can potentially benefit terminal productivity improvement, liner schedule optimization, and regional economic development planning. In order to evaluate the proposed framework, we conduct extensive studies on large-scale real-world GPS traces of container ships collected from major container ports worldwide through the year, as well as various maritime open data sources concerning ships and ports. Evaluation results confirm that the proposed framework not only can accurately estimate various port performance indicators but also effectively produces port comparison results such as port performance ranking and port region comparison.
Visibility in bad weather from a single image
(2008)

Bad weather, such as fog and haze, can significantly degrade the visibility of a scene. Optically, this is due to the substantial presence of particles in the atmosphere that absorb and scatter light. In computer vision, the absorption and scattering processes are commonly modeled by a linear combination of the direct attenuation and the airlight. Based on this model, a few methods have been proposed, and most of them require multiple input images of a scene, which have either different degrees of polarization or different atmospheric conditions. This requirement is the main drawback of these methods, since in many situations, it is difficult to be fulfilled. To resolve the problem, we introduce an automated method that only requires a single input image. This method is based on two basic observations: first, images with enhanced visibility (or clear-day images) have more contrast than images plagued by bad weather; second, airlight whose variation mainly depends on the distance of objects to the viewer, tends to be smooth. Relying on these two observations, we develop a cost function in the framework of Markov random fields, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation. The method does not require the geometrical information of the input image, and is applicable for both color and gray images.